# Getting Started¶

## Installation¶

alibi-detect can be installed from PyPI:

pip install alibi-detect


## Features¶

Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. Alibi Detect does however not install PyTorch for you. Check the PyTorch docs how to do this.

To get a list of respectively the latest outlier, adversarial and drift detection algorithms, you can type:

import alibi_detect
alibi_detect.od.__all__

['OutlierAEGMM',
'IForest',
'Mahalanobis',
'OutlierAE',
'OutlierVAE',
'OutlierVAEGMM',
'OutlierProphet',  # requires prophet: pip install alibi-detect[prophet]
'OutlierSeq2Seq',
'SpectralResidual',
'LLR']

alibi_detect.ad.__all__

['AdversarialAE',
'ModelDistillation']

alibi_detect.cd.__all__

['ChiSquareDrift',
'ClassifierDrift',
'ClassifierUncertaintyDrift',
'KSDrift',
'LearnedKernelDrift',
'LSDDDrift',
'LSDDDriftOnline',
'MMDDrift',
'MMDDriftOnline',
'RegressorUncertaintyDrift',
'SpotTheDiffDrift',
'TabularDrift']


Summary tables highlighting the practical use cases for all the algorithms can be found here.

For detailed information on the outlier detectors:

And data drift:

## Basic Usage¶

We will use the VAE outlier detector to illustrate the usage of outlier and adversarial detectors in alibi-detect.

First, we import the detector:

from alibi_detect.od import OutlierVAE


Then we initialize it by passing it the necessary arguments:

od = OutlierVAE(
threshold=0.1,
encoder_net=encoder_net,
decoder_net=decoder_net,
latent_dim=1024
)


Some detectors require an additional .fit step using training data:

od.fit(X_train)


The detectors can be saved or loaded as described in Saving and loading. Finally, we can make predictions on test data and detect outliers or adversarial examples.

preds = od.predict(X_test)


The predictions are returned in a dictionary with as keys meta and data. meta contains the detector’s metadata while data is in itself a dictionary with the actual predictions (and other relevant values). It has either is_outlier, is_adversarial or is_drift (filled with 0’s and 1’s) as well as optional instance_score, feature_score or p_value as keys with numpy arrays as values.

The exact details will vary slightly from method to method, so we encourage the reader to become familiar with the types of algorithms supported in alibi-detect.